STATISTICAL METHODS FOR STUDYING DISEASE GENE HISTORY

点击翻译按钮获取中文摘要

DESCRIPTION: (Applicant's abstract) The Human Genome Project has already produced numerous long human DNA sequences. Such a long sequence may cover an entire gene and also its flanking regions. This progress and the advent of rapid DNA sequencing techniques will allow the sequencing of large DNA regions of samples from human populations, especially regions containing a disease-causing mutation. Alternatively, one may study microsatellite markers closely linked to the mutation. The goal of this project is to develop population genetics theory, statistical methods and computer algorithms for studying the history and the mechanism of maintenance of a disease-causing mutation in a population. The specific aims are (1) to develop a coalescent theory for a DNA region subject to point mutation, natural selection and recombination, (2) to develop a coalescent theory for microsatellite loci subject to stepwise mutation, natural selection and recombination. Current coalescent methods assume an equilibrium population, and are not appropriate for studying a recent mutation subject to natural selection, such as the AF508 mutation of cystic fibrosis. We propose to develop a coalescent approach incorporating a method for simulating the history of the subpopulation bearing the mutant. (3) to develop methods for estimating the age of a disease causing mutation from DNA sequences or from microsatellite markers. (4) to develop methods for estimating the selection coefficients and for testing hypotheses about the selection coefficients of a disease-causing mutation. One of the methods we will develop is for site-by-site polymorphism data, and does not require the determination of DNA haplotype sequences. Therefore, the method will be particularly useful when large scale screening of polymorphisms in a sample is done by DNA "chips". (5) to develop a computer package for the above methods accessible on the World Wide Web, and (6) to apply the theory and methods to estimate the age and selection coefficients of the AF508 mutant of cystic fibrosis.